Deep Learning - Convolutional Neural Networks with TensorFlow

TensorFlow is the world’s most popular library for deep learning, and it is built by Google. It is the library of choice for many companies doing AI (Artificial Intelligence) and machine learning. So, if you want to do deep learning, you must know TensorFlow.

In this course, you will learn how to use TensorFlow 2 to build convolutional neural networks (CNN). We will first start by having an in-depth look at what convolution is, why it is useful, and how to integrate it into a neural network. Then you will learn how to apply CNNs to several practical image recognition datasets, from small and relatively simple to large and complex. Next, you will learn how to perform text preprocessing and text classification with CNNs

In the last section, you will learn about techniques that help improve performance, such as batch normalization, data augmentation, and transfer learning for Computer Vision.

By the end of this course, we will have understood how to build convolutional neural networks in deep learning with TensorFlow.

What you Will Learn

• Understand the concept of convolution
• Integrate convolution into neural networks
• Apply CNNs to several image recognition datasets, both small and large
• Learn best practices for designing CNN architectures
• Learn about batch normalization and data augmentation
• Learn how to preform text preprocessing

Audience

This course is designed for anyone interested in deep learning and machine learning or for anyone who wants to implement convolutional neural networks in TensorFlow 2.

One must have decent Python programming skills, should know how to build a feedforward ANN (Artificial Neural Network) in TensorFlow 2, and must have experience with data science libraries such as NumPy and Matplotlib.

About The Author

Lazy Programmer: The Lazy Programmer is an AI and machine learning engineer with a focus on deep learning, who also has experience in data science, big data engineering, and full-stack software engineering. With a background in computer engineering and specialization in machine learning, he holds two master’s degrees in computer engineering and statistics with applications to financial engineering. His expertise in online advertising and digital media includes work as both a data scientist and big data engineer.

He has created deep learning models for prediction and has experience in recommendation systems using reinforcement learning and collaborative filtering. He is a skilled instructor who has taught at universities including Columbia, NYU, Hunter College, and The New School. He has web programming expertise, with experience in technologies such as Python, Ruby/Rails, PHP, and Angular, and has provided his services to multiple businesses.